Reinforcement Learning in Multi-agent Games
نویسنده
چکیده
This article investigates the performance of independent reinforcement learners in multiagent games. Convergence to Nash equilibria and parameter settings for desired learning behavior are discussed for Q-learning, Frequency Maximum Q value (FMQ) learning and lenient Q-learning. FMQ and lenient Q-learning are shown to outperform regular Q-learning significantly in the context of coordination games with miscoordination penalties. Furthermore, Qlearning with an -greedy and FMQ learning with a Boltzmann action selection are shown to scale well to games with one thousand agents.
منابع مشابه
Utilizing Generalized Learning Automata for Finding Optimal Policies in MMDPs
Multi agent Markov decision processes (MMDPs), as the generalization of Markov decision processes to the multi agent case, have long been used for modeling multi agent system and are used as a suitable framework for Multi agent Reinforcement Learning. In this paper, a generalized learning automata based algorithm for finding optimal policies in MMDP is proposed. In the proposed algorithm, MMDP ...
متن کاملMarkov Games of Incomplete Information for Multi-Agent Reinforcement Learning
Partially observable stochastic games (POSGs) are an attractive model for many multi-agent domains, but are computationally extremely difficult to solve. We present a new model, Markov games of incomplete information (MGII) which imposes a mild restriction on POSGs while overcoming their primary computational bottleneck. Finally we show how to convert a MGII into a continuous but bounded fully ...
متن کاملMulitagent Reinforcement Learning in Stochastic Games with Continuous Action Spaces
We investigate the learning problem in stochastic games with continuous action spaces. We focus on repeated normal form games, and discuss issues in modelling mixed strategies and adapting learning algorithms in finite-action games to the continuous-action domain. We applied variable resolution techniques to two simple multi-agent reinforcement learning algorithms PHC and MinimaxQ. Preliminary ...
متن کاملReinforcement Learning in Cooperative Multi–Agent Systems
Reinforcement Learning is used in cooperative multi–agent systems differently for various problems. We provide a review on learning algorithms used for repeated common–payoff games, and stochastic general– sum games. Then these learning algorithms is compared with another algorithm for the credit assignment problem that attempts to correctly assign agents the awards that they deserve.
متن کاملMultiagent Reinforcement Learning in Stochastic Games
We adopt stochastic games as a general framework for dynamic noncooperative systems. This framework provides a way of describing the dynamic interactions of agents in terms of individuals' Markov decision processes. By studying this framework, we go beyond the common practice in the study of learning in games, which primarily focus on repeated games or extensive-form games. For stochastic games...
متن کامل